Compressed Sensing Channel Estimation for OTFS Modulation in Non-Integer Delay-Doppler Domain

TL;DR

This paper proposes a novel compressed sensing channel estimation method for OTFS modulation that accurately models non-integer delay-Doppler shifts, improving estimation accuracy without relying on integer approximations.

Contribution

It introduces an OMPBR algorithm for continuous DD dictionary estimation, addressing non-integer shifts in OTFS channels for the first time.

Findings

Reduces normalized mean squared error significantly.

Handles non-integer delay-Doppler shifts accurately.

Maintains reasonable computational complexity.

Abstract

This paper introduces a Compressed Sensing (CS) estimation scheme for Orthogonal Time Frequency Space (OTFS) channels with sparse multipath. The OTFS waveform represents signals in a two dimensional Delay-Doppler (DD) orthonormal basis. The proposed model does not require the assumption that the delays are integer multiples of the sampling period. The analysis shows that non-integer delay and Doppler shifts in the channel cannot be accurately modelled by integer approximations. An Orthogonal Matching Pursuit with Binary-division Refinement (OMPBR) estimation algorithm is proposed. The proposed estimator finds the best channel approximation over a continuous DD dictionary without integer approximations. This results in a significant reduction of the estimation normalized mean squared error with reasonable computational complexity.